AI trip planners, what they actually do well

What AI is genuinely good at in trip planning

Sequencing. Given a list of things you want to do in a city, working out a sensible order that minimises backtracking is a real problem that AI solves well and that people solve badly.

Time budgeting. Knowing that a museum plus lunch plus a walk across town is four hours, not two, and that a day with six items in it will not happen.

Filling gaps. If you have booked two dinners and one museum and have three empty days, generating candidate options for the gaps is genuinely useful.

Adapting to constraints. Reworking a plan around a group with one person who cannot walk far, a fixed check-in time, or a rainy Tuesday is exactly the kind of tedious rearrangement it does well.

What it is consistently bad at

Knowing what exists right now. A model trained on text has no reliable knowledge of whether a restaurant closed last year, whether a museum has changed its opening days, or whether the ferry still runs in October.

Prices. Any specific price in an AI-generated itinerary should be treated as an estimate at best.

Local nuance that is not written down. The fact that a particular street is unpleasant after dark, that a market is dead on Mondays, or that a viewpoint is mobbed at sunset and empty at 08:00.

Confident invention. This is the important one. When it does not know, it will often produce a plausible-sounding name and address rather than saying so. A restaurant that does not exist, at an address that does, is a specific and common failure.

• Verify every opening time against the venue own site or listing
• Treat every price as an estimate
• Check that named venues actually exist before building a day around one
• Be sceptical of anything described as a hidden local favourite
• Seasonal closures and ferry schedules are where errors cluster

How to use one properly

Treat the output as a draft, not a plan. The value is in getting from a blank page to a structured week in two minutes, which is the hardest part of planning for most people.

Then do the verification pass. Go through the itinerary and check anything with an address, a time, or a price. This takes twenty minutes and it is the difference between a useful plan and a day standing outside a closed museum.

Give it real constraints. The quality of the output tracks the quality of the input almost linearly. Dates, budget per person, group size, walking tolerance, what you have already booked, and what the group explicitly does not want are all worth stating.

Ask it to rework rather than starting again. Iterating on a draft with specific corrections produces much better results than regenerating from scratch.

What to actually look for in a trip planning tool

Whether the output is editable. An itinerary you cannot change is a document, not a plan. Trips change constantly, and a tool that produces a fixed block of text is worse than a notes app.

Whether it is shareable and collaborative. For any trip involving more than one person, this matters more than the generation quality. The hard problem in group travel is not producing an itinerary, it is getting six people to agree on one and then all have access to it.

Whether it handles the boring parts: booking references, flight times, who is arriving when, what has been paid for and by whom.

Whether it works offline or at least degrades gracefully. A plan you cannot open at an airport with no roaming is not a plan.

• Editable output, not a fixed document
• Real sharing, so the whole group sees the same version
• Somewhere to hold bookings and confirmation numbers
• Works when the connection does not
• Exports to something you own, so you are not locked in

Agoroam: an AI trip planner built for the group case

Most AI trip planners are single-player: one person prompts, one person gets an itinerary, and the group problem starts the moment that itinerary needs six people to agree on it. Agoroam is built for the group case from the ground up instead of bolting sharing onto a solo tool afterward.

The flow is three parts. Activities get proposed as cards and voted on with four reactions, veto, dislike, like, super-like, so a plan reflects what the group actually wants rather than what one person guessed. The itinerary then assembles itself from the cards that won the vote, slotted into days and time blocks automatically. And the expenses tab lives inside the same trip as the vote and the itinerary, so cost tracking is not a second app the group has to remember to open.

The practical difference from prompting a general AI assistant: the AI still drafts the first pass, but the group vote is what actually decides the trip, not whoever typed the prompt. That is the part solo AI planners have no answer for.

The group problem AI does not solve

For a solo traveller, generating an itinerary is most of the job. For a group, it is a small part of it.

The actual difficulty in group travel is decision-making: agreeing dates when six calendars conflict, agreeing a budget when nobody wants to say their number out loud, choosing between three destinations without a two-week argument, and tracking who paid for what.

No amount of itinerary generation touches any of that. A tool that helps a group vote, decide, and settle up is solving the real problem, and the AI-generated schedule is the easy half.

A sensible workflow

Lock the dates first, with a hard deadline for responses. Then agree a per-person budget. Then shortlist two or three destinations and vote, rather than debating openly.

Once the destination is fixed, generate a draft itinerary with the real constraints stated. Verify anything with a time or a price. Share it with the group and let people add and remove.

Then book the things that need booking: flights, accommodation, one activity, and one dinner per night. Leave the rest of the days deliberately open.

And put the money in a shared ledger from the first payment, not from the last night.